from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(other_library="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 14 predictions (58.33%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	KNeighborsClassifier_brute_force	predict	0.024	100000	1000	100	1.903170	0.139840	NaN	0.000420	0.001903	brute	-1	1	0.663	0.191681	0.006120	0.687	9.928841	9.933901
4	KNeighborsClassifier_brute_force	predict	0.015	100000	1000	100	2.720306	0.039169	NaN	0.000294	0.002720	brute	-1	5	0.757	0.194103	0.002229	0.742	14.014728	14.015652
7	KNeighborsClassifier_brute_force	predict	0.007	100000	1000	100	2.124504	0.052952	NaN	0.000377	0.002125	brute	1	100	0.882	0.235072	0.003459	0.875	9.037667	9.038646
8	KNeighborsClassifier_brute_force	predict	1.000	100000	1	100	0.019847	0.000414	NaN	0.000040	0.019847	brute	1	100	1.000	0.007879	0.000206	0.000	2.519058	2.519921
10	KNeighborsClassifier_brute_force	predict	0.007	100000	1000	100	3.008091	0.148458	NaN	0.000266	0.003008	brute	-1	100	0.882	0.233162	0.003925	0.875	12.901271	12.903099
11	KNeighborsClassifier_brute_force	predict	1.000	100000	1	100	0.024435	0.002458	NaN	0.000033	0.024435	brute	-1	100	1.000	0.007843	0.000935	0.000	3.115564	3.137621
13	KNeighborsClassifier_brute_force	predict	0.015	100000	1000	100	2.181953	0.055554	NaN	0.000367	0.002182	brute	1	5	0.757	0.192038	0.002864	0.742	11.362097	11.363360
16	KNeighborsClassifier_brute_force	predict	0.024	100000	1000	100	1.151653	0.005095	NaN	0.000695	0.001152	brute	1	1	0.663	0.189789	0.004287	0.687	6.068054	6.069602
19	KNeighborsClassifier_brute_force	predict	0.071	100000	1000	2	1.674027	0.026270	NaN	0.000010	0.001674	brute	-1	1	0.896	0.031264	0.000521	0.967	53.544528	53.551971
22	KNeighborsClassifier_brute_force	predict	0.052	100000	1000	2	2.699315	0.041566	NaN	0.000006	0.002699	brute	-1	5	0.922	0.031753	0.001678	0.974	85.008693	85.127369
25	KNeighborsClassifier_brute_force	predict	0.046	100000	1000	2	2.024135	0.018976	NaN	0.000008	0.002024	brute	1	100	0.929	0.071214	0.003007	0.975	28.423404	28.448740
28	KNeighborsClassifier_brute_force	predict	0.046	100000	1000	2	2.792837	0.028439	NaN	0.000006	0.002793	brute	-1	100	0.929	0.070517	0.004555	0.975	39.605094	39.687637
31	KNeighborsClassifier_brute_force	predict	0.052	100000	1000	2	2.034647	0.021317	NaN	0.000008	0.002035	brute	1	5	0.922	0.031527	0.001002	0.974	64.535910	64.568520
34	KNeighborsClassifier_brute_force	predict	0.071	100000	1000	2	1.066464	0.011803	NaN	0.000015	0.001066	brute	1	1	0.896	0.028986	0.001206	0.967	36.792839	36.824654

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	6.983	0.0	-1	1	0.048	0.005	0.238	0.239	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.001	7.156	0.0	-1	5	0.046	0.001	0.240	0.241	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.909	0.0	1	100	0.046	0.001	0.250	0.250	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.136	0.0	-1	100	0.047	0.001	0.238	0.238	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.681	0.0	1	5	0.046	0.001	0.261	0.261	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	6.962	0.0	1	1	0.046	0.000	0.248	0.248	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.334	0.0	-1	1	0.009	0.000	0.523	0.524	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.333	0.0	-1	5	0.009	0.000	0.518	0.518	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.313	0.0	1	100	0.009	0.000	0.558	0.558	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.342	0.0	-1	100	0.009	0.001	0.520	0.521	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.329	0.0	1	5	0.009	0.000	0.534	0.534	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.330	0.0	1	1	0.009	0.000	0.530	0.531	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.903	0.140	0.000	0.002	-1	1	0.192	0.006	9.929	9.934	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.002	0.000	0.022	-1	1	0.008	0.000	2.748	2.749	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.720	0.039	0.000	0.003	-1	5	0.194	0.002	14.015	14.016	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.026	0.004	0.000	0.026	-1	5	0.007	0.000	3.529	3.530	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.125	0.053	0.000	0.002	1	100	0.235	0.003	9.038	9.039	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.020	0.000	0.000	0.020	1	100	0.008	0.000	2.519	2.520	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	3.008	0.148	0.000	0.003	-1	100	0.233	0.004	12.901	12.903	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.002	0.000	0.024	-1	100	0.008	0.001	3.116	3.138	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.182	0.056	0.000	0.002	1	5	0.192	0.003	11.362	11.363	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.020	0.001	0.000	0.020	1	5	0.008	0.000	2.576	2.576	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.152	0.005	0.001	0.001	1	1	0.190	0.004	6.068	6.070	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.020	0.002	0.000	0.020	1	1	0.008	0.000	2.499	2.500	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.674	0.026	0.000	0.002	-1	1	0.031	0.001	53.545	53.552	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.005	0.002	0.000	0.005	-1	1	0.001	0.000	6.352	6.372	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.699	0.042	0.000	0.003	-1	5	0.032	0.002	85.009	85.127	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.002	0.000	0.007	-1	5	0.001	0.000	9.126	9.139	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.024	0.019	0.000	0.002	1	100	0.071	0.003	28.423	28.449	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	3.789	3.796	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.793	0.028	0.000	0.003	-1	100	0.071	0.005	39.605	39.688	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.008	0.002	0.000	0.008	-1	100	0.001	0.000	9.654	9.688	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.035	0.021	0.000	0.002	1	5	0.032	0.001	64.536	64.569	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	4.240	4.255	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.066	0.012	0.000	0.001	1	1	0.029	0.001	36.793	36.825	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.896	2.907	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 12 predictions (50.0%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	KNeighborsClassifier_kd_tree	predict	0.019	1000000	1000	10	0.830243	0.991239	NaN	0.000096	0.000830	kd_tree	-1	1	0.929	0.106964	0.001710	0.910	7.761857	7.762849
4	KNeighborsClassifier_kd_tree	predict	0.005	1000000	1000	10	0.976204	0.330636	NaN	0.000082	0.000976	kd_tree	-1	5	0.946	0.188695	0.009525	0.941	5.173455	5.180042
7	KNeighborsClassifier_kd_tree	predict	0.011	1000000	1000	10	5.363226	0.571489	NaN	0.000015	0.005363	kd_tree	1	100	0.951	0.577475	0.019306	0.940	9.287374	9.292563
10	KNeighborsClassifier_kd_tree	predict	0.011	1000000	1000	10	3.068582	0.125361	NaN	0.000026	0.003069	kd_tree	-1	100	0.951	0.542834	0.013457	0.940	5.652891	5.654627
13	KNeighborsClassifier_kd_tree	predict	0.005	1000000	1000	10	1.530345	0.213749	NaN	0.000052	0.001530	kd_tree	1	5	0.946	0.195760	0.002786	0.941	7.817468	7.818260
16	KNeighborsClassifier_kd_tree	predict	0.019	1000000	1000	10	0.855822	0.118249	NaN	0.000093	0.000856	kd_tree	1	1	0.929	0.100967	0.000686	0.910	8.476236	8.476432
19	KNeighborsClassifier_kd_tree	predict	0.012	1000	1000	2	0.028518	0.012147	NaN	0.000561	0.000029	kd_tree	-1	1	0.891	0.000436	0.000055	0.879	65.476509	65.995831
22	KNeighborsClassifier_kd_tree	predict	0.006	1000	1000	2	0.024493	0.001280	NaN	0.000653	0.000024	kd_tree	-1	5	0.911	0.000707	0.000028	0.905	34.655740	34.683040
25	KNeighborsClassifier_kd_tree	predict	0.023	1000	1000	2	0.036097	0.004484	NaN	0.000443	0.000036	kd_tree	1	100	0.894	0.005096	0.000137	0.917	7.082817	7.085362
28	KNeighborsClassifier_kd_tree	predict	0.023	1000	1000	2	0.040282	0.004597	NaN	0.000397	0.000040	kd_tree	-1	100	0.894	0.005838	0.001516	0.917	6.900018	7.128774
31	KNeighborsClassifier_kd_tree	predict	0.006	1000	1000	2	0.020997	0.000622	NaN	0.000762	0.000021	kd_tree	1	5	0.911	0.000714	0.000027	0.905	29.404205	29.424527
34	KNeighborsClassifier_kd_tree	predict	0.012	1000	1000	2	0.019529	0.000828	NaN	0.000819	0.000020	kd_tree	1	1	0.891	0.000431	0.000067	0.879	45.294086	45.840846

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2.926	0.089	0.027	0.0	-1	1	0.807	0.076	3.624	3.640	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.686	0.145	0.022	0.0	-1	5	0.727	0.014	5.073	5.074	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.588	0.141	0.022	0.0	1	100	0.831	0.043	4.318	4.324	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.674	0.087	0.022	0.0	-1	100	0.723	0.012	5.082	5.083	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.682	0.226	0.022	0.0	1	5	0.822	0.036	4.478	4.482	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.552	0.054	0.023	0.0	1	1	0.749	0.017	4.744	4.745	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.020	0.0	-1	1	0.004	0.002	0.221	0.261	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.024	0.0	-1	5	0.001	0.000	0.613	0.642	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.026	0.0	1	100	0.001	0.001	0.523	0.638	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.028	0.0	-1	100	0.001	0.000	0.594	0.605	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.029	0.0	1	5	0.001	0.000	0.556	0.568	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.027	0.0	1	1	0.001	0.000	0.654	0.654	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.830	0.991	0.000	0.001	-1	1	0.107	0.002	7.762	7.763	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	1	0.000	0.000	8.999	9.243	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.976	0.331	0.000	0.001	-1	5	0.189	0.010	5.173	5.180	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.000	0.000	8.703	9.020	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.363	0.571	0.000	0.005	1	100	0.577	0.019	9.287	9.293	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	1	100	0.001	0.000	4.396	4.508	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.069	0.125	0.000	0.003	-1	100	0.543	0.013	5.653	5.655	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	6.282	6.459	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.530	0.214	0.000	0.002	1	5	0.196	0.003	7.817	7.818	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.001	0.000	0.002	1	5	0.000	0.000	3.600	3.701	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.856	0.118	0.000	0.001	1	1	0.101	0.001	8.476	8.476	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	3.893	4.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.029	0.012	0.001	0.000	-1	1	0.000	0.000	65.477	65.996	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	1	0.000	0.000	26.039	27.120	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.024	0.001	0.001	0.000	-1	5	0.001	0.000	34.656	34.683	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	25.918	26.703	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.036	0.004	0.000	0.000	1	100	0.005	0.000	7.083	7.085	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	5.906	6.137	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.040	0.005	0.000	0.000	-1	100	0.006	0.002	6.900	7.129	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.001	0.000	0.003	-1	100	0.000	0.000	23.624	24.425	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.021	0.001	0.001	0.000	1	5	0.001	0.000	29.404	29.425	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	5.624	5.911	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.020	0.001	0.001	0.000	1	1	0.000	0.000	45.294	45.841	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	5.804	6.186	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.552	0.083	30	0.029	0.0	random	0.489	0.049	1.129	1.135	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.619	0.025	30	0.026	0.0	k-means++	0.515	0.039	1.202	1.205	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.719	0.177	30	0.140	0.0	random	2.796	0.089	2.046	2.047	See	See
9	KMeans_tall	fit	1000000	1000000	100	6.265	0.240	30	0.128	0.0	k-means++	3.116	0.179	2.011	2.014	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.009	0.000	random	0.0	0.0	10.405	13.726	See	See
2	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	random	0.0	0.0	8.422	12.417	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.008	0.000	k-means++	0.0	0.0	11.611	12.344	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	k-means++	0.0	0.0	13.213	13.888	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.414	0.000	random	0.0	0.0	7.008	7.315	See	See
8	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.001	0.002	random	0.0	0.0	12.193	12.594	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.403	0.000	k-means++	0.0	0.0	6.328	6.568	See	See
11	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	k-means++	0.0	0.0	10.311	11.540	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 4 predictions (50.0%).
The observed differences can be found in the diff_adjusted_rand_scores column.
The chosen difference threshold is 0.001 for adjusted_rand_score.
See details in the dataframe below.

	estimator	function	diff_adjusted_rand_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup
1	KMeans_short	predict	0.001090	10000	1000	2	0.002234	0.000168	20	0.007162	0.000002	full	random	20	300	1	1.000000e-16	0.000126	0.000649	0.000112	-0.000965	3.439828	3.490900
4	KMeans_short	predict	0.001995	10000	1000	2	0.002404	0.000220	20	0.006657	0.000002	full	k-means++	20	300	1	1.000000e-16	0.001245	0.000719	0.000153	-0.000750	3.344541	3.419384
7	KMeans_short	predict	0.015034	10000	1000	100	0.003165	0.000233	20	0.252798	0.000003	full	random	20	300	1	1.000000e-16	0.278733	0.001316	0.000090	0.293767	2.404209	2.409801
10	KMeans_short	predict	0.060044	10000	1000	100	0.003253	0.000459	20	0.245923	0.000003	full	k-means++	20	300	1	1.000000e-16	0.317011	0.001297	0.000074	0.256968	2.508664	2.512745

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.109	0.005	20	0.001	0.0	random	0.038	0.007	2.846	2.891	See	See
3	KMeans_short	fit	10000	10000	2	0.302	0.006	20	0.001	0.0	k-means++	0.107	0.007	2.809	2.814	See	See
6	KMeans_short	fit	10000	10000	100	0.280	0.010	20	0.029	0.0	random	0.158	0.004	1.771	1.772	See	See
9	KMeans_short	fit	10000	10000	100	0.822	0.034	20	0.010	0.0	k-means++	0.413	0.025	1.988	1.992	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.007	0.000	random	0.001	0.0	3.440	3.491	See	See
2	KMeans_short	predict	10000	1	2	0.002	0.0	20	0.000	0.002	random	0.000	0.0	8.719	10.971	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.007	0.000	k-means++	0.001	0.0	3.345	3.419	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.0	20	0.000	0.002	k-means++	0.000	0.0	12.588	12.933	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.253	0.000	random	0.001	0.0	2.404	2.410	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.000	0.002	random	0.000	0.0	9.490	9.752	See	See
10	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.246	0.000	k-means++	0.001	0.0	2.509	2.513	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.000	0.002	k-means++	0.000	0.0	10.260	10.488	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 2 predictions (50.0%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	LogisticRegression	predict	0.01	1000000	1000	100	0.000429	0.000356	[20]	1.866212	4.286758e-07	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.56	0.001029	0.001924	0.55	0.416504	0.883111
4	LogisticRegression	predict	0.07	1000	100	10000	0.002234	0.000297	[26]	3.581245	2.233860e-05	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.35	0.005966	0.000738	0.28	0.374448	0.377304

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	12.340	0.283	[20]	0.065	0.000	2.102	0.058	5.870	5.873	See	See
3	LogisticRegression	fit	1000	1000	10000	1.072	0.722	[26]	0.075	0.001	0.799	0.056	1.342	1.345	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	1.866	0.0	0.001	0.002	0.417	0.883	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.012	0.0	0.000	0.000	0.406	0.411	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	3.581	0.0	0.006	0.001	0.374	0.377	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	0.776	0.0	0.002	0.000	0.060	0.060	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 1 prediction (25.0%).
The observed differences can be found in the diff_r2_scores column.
The chosen difference threshold is 0.001 for r2_score.
See details in the dataframe below.

	estimator	function	diff_r2_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup
1	Ridge	predict	0.039624	1000	1000	10000	0.009627	0.000268	NaN	8.310143	0.00001	1.0	True	True	NaN	deprecated	NaN	auto	0.001	0.082567	0.016435	0.000283	0.122191	0.585754	0.585841

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.181	0.006	0.441	0.0	0.197	0.005	0.919	0.920	See	See
3	Ridge	fit	1000000	1000000	100	1.284	0.119	0.623	0.0	0.315	0.148	4.078	4.507	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.01	0.0	8.310	0.0	0.016	0.0	0.586	0.586	See	See
2	Ridge	predict	1000	1	10000	0.00	0.0	1.246	0.0	0.000	0.0	0.643	0.689	See	See
4	Ridge	predict	1000000	1000	100	0.00	0.0	4.888	0.0	0.000	0.0	0.423	0.652	See	See
5	Ridge	predict	1000000	1	100	0.00	0.0	0.015	0.0	0.000	0.0	0.583	0.614	See	See